{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 接收字符串的input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please tell me someone I should know: 2\n",
      "['guido', 'tim', 'jesse', '2']\n"
     ]
    }
   ],
   "source": [
    "# Start with a list containing several names.\n",
    "names = ['guido', 'tim', 'jesse']\n",
    "\n",
    "# Ask the user for a name.\n",
    "new_name = input(\"Please tell me someone I should know: \")\n",
    "\n",
    "# Add the new name to our list.\n",
    "names.append(new_name)\n",
    "\n",
    "# Show that the name has been added to the list.\n",
    "print(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(names[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获得数值的int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你体重多少?按斤计算 一百斤\n",
      "<class 'str'>\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: '一百斤'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-0efb47d94e79>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"你体重多少?按斤计算 \"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: '一百斤'"
     ]
    }
   ],
   "source": [
    "height = input(\"你体重多少?按斤计算 \")\n",
    "print(type(height))\n",
    "height = int(height)\n",
    "print(type(height))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 如果用户输入了字串了怎么办？比如输了“一百斤”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你体重多少?按斤计算 yibai\n",
      "请输入阿拉伯数字输入体重erbai\n",
      "请输入阿拉伯数字输入体重100\n",
      "<class 'int'>\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "height = input(\"你体重多少?按斤计算 \")\n",
    "\n",
    "while height.isdigit() == False:\n",
    "    height = input(\"请输入阿拉伯数字输入体重\")\n",
    "    \n",
    "height = int(height)   \n",
    "print(type(height))\n",
    "print(height)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(\"Give me two numbers, and I'll divide them.\")\n",
    "print(\"Enter 'q' to quit.\")\n",
    "\n",
    "while True:\n",
    "    first_number = input(\"\\nFirst number: \")\n",
    "    if first_number == 'q':\n",
    "        break\n",
    "    second_number = input(\"Second number: \")\n",
    "    try:\n",
    "        answer = int(first_number) / int(second_number)\n",
    "    except ZeroDivisionError:\n",
    "        print(\"You can't divide by 0!\")\n",
    "    else:\n",
    "        print(answer)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获得数值的float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ isdigit()函数只能检验是否整数，不能检验输入的字符串是否浮点数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "a = '4'\n",
    "print(a.isdigit())\n",
    "\n",
    "a = '4.2'\n",
    "print(a.isdigit())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 自定义一个函数来判断是否浮点数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "def isFloat(string):\n",
    "    is_float=1\n",
    "    try:\n",
    "        float(string)\n",
    "    except:\n",
    "        is_float=0\n",
    "    return is_float\n",
    "\n",
    "GPA = '5.2'\n",
    "print(isFloat(GPA))\n",
    "\n",
    "\n",
    "GPA = '五点二'\n",
    "print(isFloat(GPA))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**处理异常**\n",
    "\n",
    "+ 如果你在写python程序时遇到异常后想进行如下处理的话,一般用try来处理异常;\n",
    "\n",
    "try:  \n",
    "    语句1  \n",
    "    语句2  \n",
    "    .  \n",
    "    .  \n",
    "    语句N  \n",
    "except .........:  \n",
    "    do something .......  \n",
    "\n",
    "\n",
    "+ 但是你并不知道\"语句1至语句N\"在执行会出什么样的异常，但你还要做异常处理;\n",
    "+ 所以在\"except ......\"这句应怎样来写呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "请输入你的加权分数，要求精确到小数点后1位 4.3\n",
      "<class 'float'>\n",
      "4.3\n"
     ]
    }
   ],
   "source": [
    "def isFloat(string):\n",
    "    is_float=1\n",
    "    try:\n",
    "        float(string)\n",
    "    except:\n",
    "        is_float=0\n",
    "    return is_float\n",
    "\n",
    "GPA = input(\"请输入你的加权分数，要求精确到小数点后1位 \")\n",
    "\n",
    "\n",
    "while isFloat(GPA) == 0:\n",
    "    GPA = input(\"请输入阿拉伯数字形式的加权分数，要求精确到小数点后1位 \")\n",
    "    \n",
    "GPA = float(GPA)    \n",
    "print(type(GPA))\n",
    "print(GPA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 以上都是对程序员友好，对用户不友好的表现\n",
    "+ 真正的友好就是输啥都能识别，那就需要了解更多Python了，尤其是正则表达式的部分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## python可以打开的文件类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 逗号分隔值（CSV）\n",
    "+ XLSX\n",
    "+ 纯文本（txt）\n",
    "+ JSON\n",
    "+ 图像\n",
    "+ ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>14.7</td>\n",
       "      <td>66</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>18.5</td>\n",
       "      <td>73</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>25.9</td>\n",
       "      <td>41</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>aug</td>\n",
       "      <td>sat</td>\n",
       "      <td>25.9</td>\n",
       "      <td>32</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>19.5</td>\n",
       "      <td>39</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  Y month  day  temp  RH  wind  rain  area\n",
       "0  1  2   aug  fri  14.7  66   2.7   0.0   0.0\n",
       "1  1  2   aug  fri  18.5  73   8.5   0.0   0.0\n",
       "2  1  2   aug  fri  25.9  41   3.6   0.0   0.0\n",
       "3  1  2   aug  sat  25.9  32   3.1   0.0   0.0\n",
       "4  1  2   aug  sun  19.5  39   6.3   0.0   0.0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('ForestData.csv')\n",
    "df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### xlsx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>14.7</td>\n",
       "      <td>66</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>18.5</td>\n",
       "      <td>73</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>25.9</td>\n",
       "      <td>41</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>aug</td>\n",
       "      <td>sat</td>\n",
       "      <td>25.9</td>\n",
       "      <td>32</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>19.5</td>\n",
       "      <td>39</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  Y month  day  temp  RH  wind  rain  area\n",
       "0  1  2   aug  fri  14.7  66   2.7   0.0   0.0\n",
       "1  1  2   aug  fri  18.5  73   8.5   0.0   0.0\n",
       "2  1  2   aug  fri  25.9  41   3.6   0.0   0.0\n",
       "3  1  2   aug  sat  25.9  32   3.1   0.0   0.0\n",
       "4  1  2   aug  sun  19.5  39   6.3   0.0   0.0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_excel('ForestData.xlsx')\n",
    "df2.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipaxing = open('pipaxing.txt', \"r\")\n",
    "lines = pipaxing.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'琵琶行  白居易\\n\\n浔阳江头夜送客，枫叶荻花秋瑟瑟。主人下马客在船，举酒欲饮无管弦。 \\n醉不成欢惨将别，别时茫茫江浸月。忽闻水上琵琶声，主人忘归客不发。 \\n寻声暗问弹者谁？琵琶声停欲语迟。移船相近邀相见，添酒回灯重开宴。 \\n千呼万唤始出来，犹抱琵琶半遮面。转轴拨弦三两声，未成曲调先有情。\\n \\n弦弦掩抑声声思，似诉平生不得志。低眉信手续续弹，说尽心中无限事。 \\n轻拢慢捻抹复挑，初为霓裳后六幺。大弦嘈嘈如急雨，小弦切切如私语。 \\n嘈嘈切切错杂弹，大珠小珠落玉盘。间关莺语花底滑，幽咽泉流冰下难。 \\n冰泉冷涩弦凝绝，凝绝不通声渐歇。别有幽愁暗恨生，此时无声胜有声。 \\n\\n银瓶乍破水浆迸，铁骑突出刀枪鸣。曲终收拨当心画，四弦一声如裂帛。 \\n东船西舫悄无言，惟见江心秋月白。沉吟放拨插弦中，整顿衣裳起敛容。 \\n自言本是京城女，家在虾蟆陵下住。十三学得琵琶成，名属教坊第一部。 \\n曲罢曾教善才服，妆成每被秋娘妒。武陵年少争缠头，一曲红绡不知数。 \\n\\n钿头银篦击节碎，血色罗裙翻酒污。今年欢笑复明年，秋月春风等闲度。 \\n弟走从军阿姨死，暮去朝来颜色故。门前冷落车马稀，老大嫁作商人妇。 \\n商人重利轻别离，前月浮梁买茶去。去来江口求空船，绕船月明江水寒。 \\n夜深忽梦少年事，梦啼红妆泪阑干。我闻琵琶已叹息，又闻此语重唧唧。\\n \\n同是天涯沦落人，相逢何必曾相识。我从去岁辞帝京，谪居卧病浔阳城。 \\n浔阳地僻无音乐，终岁不闻丝竹声。住近湓城地低湿，黄芦苦竹绕宅生。 \\n其间旦暮闻何物？杜鹃啼血猿哀鸣。春江花朝秋月夜，往往取酒还独倾。 \\n岂无山歌与村笛？呕哑嘲哳难为听。今夜闻君琵琶语，如听仙乐耳暂明。\\n \\n莫辞更坐弹一曲，为君翻作琵琶行。感我此言良久立，却坐促弦弦转急。 \\n凄凄不似向前声，满座重闻皆掩泣。座中泣下谁最多？江州司马青衫湿'"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipaxing.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movies =pd.read_json('movies.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'id': 'tt7183578', 'Name': '无双', 'Credit': '8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'id': 'tt9034530', 'Name': '找到你', 'Credit': '...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              movies\n",
       "0  {'id': 'tt7183578', 'Name': '无双', 'Credit': '8...\n",
       "1  {'id': 'tt9034530', 'Name': '找到你', 'Credit': '..."
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PjWdpygYuRCTy4+EQetdsxgkOUILSiDMUd2uxe8qX8d23qNejUaNO4FGtGfEKeORiP9k9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\n7NwocJmqz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TGjpVS5yMxzk8kkY8e/K/KIm46XL1+WYyd/ld4DMj7acXFxmXWjR4+WkydP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FG6vVitNTdhW5DjtLlj2D1XqVbbDpx69o3iqBt995jY6d0hGLuMK6C8LOz9u33RSFviFD+5KU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\n4ZFH/H9fOCOHfG2WvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import misc\n",
    "import glob\n",
    "from skimage.io import imread\n",
    "f = glob.glob(r\"exfriend.jpg\")[0]\n",
    "im = imread(f)\n",
    "#misc.imsave('exfriend.jpg', f) # uses the Image module (PIL)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 文件的操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 打开文件用open()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 文件使用完毕后必须关闭，因为文件对象会占用操作系统的资源\n",
    "+ 并且操作系统同一时间能打开的文件数量也是有限的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1415926535\n",
      "  8979323846\n",
      "  2643383279\n",
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "I/O operation on closed file.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-efc51e685ef7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: I/O operation on closed file."
     ]
    }
   ],
   "source": [
    "lines = open('pi_digits.txt')\n",
    "print(lines.read())\n",
    "lines.close()\n",
    "print(lines.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 'r'：读，open()的默认值就是r\n",
    "+ 'w'：写，如果没有这个文件，就创建一个；如果有，就把原文件的内容清空再写入新的东西\n",
    "+ 'a'：追加，不想清空原来的内容而是直接在后面追加新的内容，就用'a'这个模式\n",
    "+ 'r+' == r+w（可读可写，文件若不存在就报错）\n",
    "+ 'w+' == w+r（可读可写，文件若不存在就创建）\n",
    "+ 'a+' ==a+r（可追加可写，文件若不存在就创建）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果是二进制文件，就都加一个b：\n",
    "+ 'rb'\n",
    "+ 'wb'\n",
    "+ 'ab'\n",
    "+ 'rb+'\n",
    "+ 'wb+'\n",
    "+ 'ab+'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 打开文件会出现IOerror"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'pi_digit.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-3a4d96b08095>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'pi_digit.txt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'pi_digit.txt'"
     ]
    }
   ],
   "source": [
    "lines = open('pi_digit.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 打开文件的过程应该是这样的：\n",
    "    + 如果能打开就打开并使用文件；\n",
    "    + 如果不能打开就报错；\n",
    "    + 如果使用了文件要记得关上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No such file or directory\n"
     ]
    }
   ],
   "source": [
    "# 类似这样\n",
    "try:\n",
    "    lines = open('pi_digit.txt')\n",
    "    print(lines.read())\n",
    "except:\n",
    "    print('No such file or directory')\n",
    "finally:\n",
    "    if lines:\n",
    "        lines.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### with open(filename) as file_object:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 但是这样很麻烦，所以python提供了**with**，让python帮你决定什么时候关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('pi_digits.txt') as file_object:\n",
    "    lines = file_object.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['3.1415926535\\n', '  8979323846\\n', '  2643383279\\n']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取文件用read"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ read() 每次读取整个文件，它通常用于将文件内容放到一个字符串变量中。\n",
    "    + 如果文件大于可用内存，为了保险起见，可以反复调用read(size)方法，每次最多读取size个字节的内容。\n",
    "+ readlines() 自动将文件内容分析成一个行的列表.\n",
    "    + 该列表可以由 Python 的 for ... in ... 结构进行处理。\n",
    "+ readline() 每次只读取一行，通常比readlines() 慢得多。\n",
    "    + 仅当没有足够内存可以一次读取整个文件时，才应该使用 readline()。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = 'pi_digits.txt'\n",
    "\n",
    "#read()\n",
    "with open(filename) as file_object:\n",
    "    lines = file_object.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.1415926535\\n  8979323846\\n  2643383279\\n'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1415926535\n",
      "  8979323846\n",
      "  2643383279\n",
      "\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(lines)\n",
    "print(type(lines))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**问题：**  \n",
    "为什么print的时候可以正常显示？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# readlines\n",
    "with open(filename) as file_object:\n",
    "    lines = file_object.readlines()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['3.1415926535\\n', '  8979323846\\n', '  2643383279\\n']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['3.1415926535\\n', '  8979323846\\n', '  2643383279\\n']\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(lines)\n",
    "print(type(lines)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# readline()\n",
    "with open(filename) as file_object:\n",
    "    lines = file_object.readline()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.1415926535\\n'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1415926535\n",
      "\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(lines)\n",
    "print(type(lines))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 通过循环读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3.1415926535\n",
      "\n",
      "2\n",
      "  8979323846\n",
      "\n",
      "3\n",
      "  2643383279\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x = 1\n",
    "with open(filename) as file_object:\n",
    "    lines = file_object.readline()  \n",
    "    while lines:\n",
    "        print(x)\n",
    "        print(lines)\n",
    "        x = x + 1\n",
    "        lines = file_object.readline()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 手动去掉‘\\n’"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I love programming.\n",
      "\n",
      "I love creating new games.\n",
      "\n",
      "I also love finding meaning in large datasets.\n",
      "\n",
      "I love creating apps that can run in a browser.\n",
      "\n",
      "I love programming.\n",
      "I love creating new games.\n",
      "I also love finding meaning in large datasets.\n",
      "I love creating apps that can run in a browser.\n"
     ]
    }
   ],
   "source": [
    "with open(filename) as file_object:\n",
    "    lines = file_object.readlines()     \n",
    "    \n",
    "for line in lines:\n",
    "    print(line)\n",
    "    \n",
    "for line in lines:\n",
    "    print(line.rstrip()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 读取中文的字符编码问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "﻿秋登兰山寄张五\n",
      "北山白云里，隐者自怡悦。\n",
      "相望试登高，心随雁飞灭。\n",
      "愁因薄暮起，兴是清秋发。\n",
      "时见归村人，沙行渡头歇。\n",
      "天边树若荠，江畔洲如月。\n",
      "何当载酒来，共醉重阳节。\n"
     ]
    }
   ],
   "source": [
    "filename = 'chinesetext.txt'\n",
    "with open(filename, encoding='UTF-8') as file_object:\n",
    "    lines = file_object.readlines()     \n",
    "\n",
    "    \n",
    "for line in lines:\n",
    "    print(line.rstrip()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 英文读取后的全文多少字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file alice.txt has about 29461 words.\n"
     ]
    }
   ],
   "source": [
    "filename = 'alice.txt'\n",
    "\n",
    "try:\n",
    "with open(filename) as f_obj:\n",
    "        contents = f_obj.read()\n",
    "except FileNotFoundError as e:\n",
    "    msg = \"Sorry, the file \" + filename + \" does not exist.\"\n",
    "    print(msg)\n",
    "else:\n",
    "    # Count the approximate number of words in the file.\n",
    "    words = contents.split()\n",
    "    num_words = len(words)\n",
    "    print(\"The file \" + filename + \" has about \" + str(num_words) + \" words.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file alice.txt has about 29461 words.\n",
      "The file siddhartha.txt has about 42172 words.\n",
      "The file little_women.txt has about 189079 words.\n"
     ]
    }
   ],
   "source": [
    "def count_words(filename):\n",
    "    \"\"\"Count the approximate number of words in a file.\"\"\"\n",
    "def count_words(filename):\n",
    "    \"\"\"Count the approximate number of words in a file.\"\"\"\n",
    "    try:\n",
    "        with open(filename) as f_obj:\n",
    "            contents = f_obj.read() \n",
    "    except FileNotFoundError:\n",
    "        pass\n",
    "    else:\n",
    "        # Count approximate number of words in the file.\n",
    "        words = contents.split()\n",
    "        num_words = len(words)\n",
    "        print(\"The file \" + filename + \" has about \" + str(num_words) + \" words.\")\n",
    "\n",
    "filenames = ['alice.txt', 'siddhartha.txt', 'moby_dick.txt', 'little_women.txt']\n",
    "for filename in filenames:\n",
    "    count_words(filename)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 你的出生日期在圆周率中？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your birthday, in the form mmddyy: 121098\n",
      "Your birthday does not appear in the first million digits of pi.\n"
     ]
    }
   ],
   "source": [
    "filename = 'pi_million_digits.txt'\n",
    "\n",
    "with open(filename) as file_object:\n",
    "    lines = file_object.readlines()\n",
    "\n",
    "pi_string = ''\n",
    "for line in lines:\n",
    "    pi_string += line.rstrip()\n",
    "\n",
    "birthday = input(\"Enter your birthday, in the form mmddyy: \")\n",
    "if birthday in pi_string:\n",
    "    print(\"Your birthday appears in the first million digits of pi!\")\n",
    "else:\n",
    "    print(\"Your birthday does not appear in the first million digits of pi.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 追加和写文件用write"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 追加：‘a’"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##### write()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ write()是将字符串写入文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I love programming.\n",
      "I love creating new games.\n"
     ]
    }
   ],
   "source": [
    "filename = 'programming.txt'\n",
    "with open(filename) as file_object:\n",
    "    lines = file_object.readlines()  \n",
    "for line in lines:\n",
    "    print(line.rstrip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 追加内容是用with打开文件\n",
    "    + 写文件时，操作系统往往不会立刻把数据写入磁盘，而是放到内存缓存起来，有空再写入\n",
    "    + 调用close()后，操作系统才把没有写入的数据全部写入磁盘\n",
    "    + 忘记调用close()，就会丢数据\n",
    "    + 用with语句就不需要担心这个问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(filename, 'a') as file_object:\n",
    "    file_object.write(\"I also love finding meaning in large datasets.\\n\")\n",
    "    file_object.write(\"I also love creating apps that can run in a browser.\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I love programming.\n",
      "I love creating new games.\n",
      "I also love finding meaning in large datasets.\n",
      "I also love creating apps that can run in a browser.\n"
     ]
    }
   ],
   "source": [
    "with open(filename) as file_object:\n",
    "    lines = file_object.readlines()  \n",
    "for line in lines:\n",
    "    print(line.rstrip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##### writelines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ writelines()是对列表的操作，把一个字符串列表添加到文件中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'programming.txt'\n",
    "with open(filename,\"r\") as f:\n",
    "    lines = f.readlines()\n",
    "with open(filename, 'w') as file_object:\n",
    "    for line in lines:\n",
    "        if \"also\" in line:\n",
    "            continue\n",
    "        file_object.write(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1415926535\n",
      "  8979323846\n",
      "  2643383279\n"
     ]
    }
   ],
   "source": [
    "filename = 'programming.txt'\n",
    "with open(filename) as file_object:\n",
    "    lines = file_object.readlines()  \n",
    "for line in lines:\n",
    "    print(line.rstrip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(filename, 'a') as file_object:\n",
    "    file_object.writelines([\"I also love finding meaning in large datasets.\\n\",\n",
    "                            \"I also love creating apps that can run in a browser.\\n\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### 写文件：'w'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 如果没有这个文件，就创建一个；\n",
    "+ 如果有，那么就清空原文件，再写入新内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('test.txt', 'w') as wt:\n",
    "    wt.write('Hello, world!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('test.txt', 'w') as wt:\n",
    "    wt.write('Hello, Python!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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   "codemirror_mode": {
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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    "left": "10px",
    "top": "150px",
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